Artificial Intelligence

On the day I joined SEMI in March of 2017, I was filled with excitement to be on-boarding at a time when great, leaping strides in innovation were driving the rapid expansion of our ecosystem. In my many conversations with members that followed, I was not surprised that a vast majority ranked among their top concerns the persistent challenge of attracting, training and retaining the talent needed to grow their businesses. Later that year, I raised the global talent shortage issue in my article Securing Talent to Connect, Collaborate and Innovate. As an industry veteran I knew that the decades-long workforce development challenge will only worsen with the proliferation and increasing complexity of technology.Innovation has never been more technology-intensive. Developing the technology and producing the components required for applications powering next-generation communications (5G), artificial intelligence (AI) and machine learning, autonomous vehicles, and the Internet of Things (IoT) require bright minds in diverse fields of science to fill critical positions in the global electronics manufacturing industry. Today, that talent struggle is acute, threatening to undermine our industry’s potential to grow to $1 trillion by 2030.The electronics industry needs a comprehensive, integrated program to build the talent pipeline. The program should inspire school-age children to adult learners to pursue careers in this great but underrecognized industry. It needs to shine a spotlight on career opportunities. It must prepare workers with standardized skills sets transferable across the industry. And it must connect trained workers with hiring companies.SEMI is uniquely positioned to deliver this solution. Launched almost two years to the day after I joined SEMI, SEMI Works is SEMI’s branded workforce development initiative. We realize that trade associations don’t create jobs. Their members do. Think of SEMI Works as SEMI’s commitment to build and maintain the needed infrastructure – the talent pipeline. SEMI Works is comprehensive. The program, supported by SEMI members, is a wide-ranging effort by our Global Advocacy team to ensure education is demand-driven, training programs better meet the needs of the industry, more people pursue careers in electronics and our members have access to the talent pool that we are cultivating. With SEMI Works, SEMI is developing scalable solutions to improve connections among training and education providers, prospective workers and the industry. Key features of SEMI Works will include SEMI-certified education courses and training programs linked to industry requirements and skills credentialing for workers.SEMI Works starts with raising awareness of SEMI-certified programs as a key bridge connecting prospective talent, the industry and applicable training and education programs. Growing awareness of the programs will enable SEMI to build an extensive database of employers and qualified talent and link both to the right training. SEMI will continue to drive and endorse programs that help meet member needs throughout the education continuum – from K-4 to higher education and adult training. But the infrastructure and ecosystem required to support and scale these programs is the key for all of us to win together. At a high level, SEMI Works consists of several important components: Linking the required industry competencies to education and training course curriculum – Similar to the establishment of SEMI standards, SEMI will certify education and training programs that dovetail with the industry competency model. Initial certification and annual re-certification ensure continued updates, relevance and sustainability of the programs. SEMI will raise awareness of SEMI Works certified programs as the standard for meeting the industry’s talent requirements. Developing and maintaining the electronics industry competency model – Through established working groups and ongoing dialogue with our members, we are developing a competency model – a tiered matrix of required competencies used to link course curriculum to the talent needs of employers. The competency model consists of interpersonal and individual skills, academic and general industry requirements, advanced manufacturing competencies, and competencies by job. SEMI will establish and maintain the model with regular updates. Improving access to talent – Through SEMI Works, SEMI will build an extensive database that brings together programs, talent and employers. People and organizations opting into a SEMI-certified program or acquiring a SEMI program certification will be part of the SEMI database. Job seekers will be able to set up a profile and resume and search for training and employment opportunities, and employers will search the talent pool – much as job-search sites work today – assured of a skills match based on the SEMI certification. I am passionate about education and proud of all of SEMI’s efforts. I am especially proud of the work we are doing to help provide a pathway to meaningful careers for children and adults all around the world. We no longer have the luxury of a piecemeal approach to training and education.It is my hope and belief that SEMI Works, together with our efforts to improve diversity and inclusion in the workforce, will be SEMI’s lasting mark on the global electronics industry.Ajit Manocha is president and CEO of SEMI.

MedTech, autonomous driving and other disruptive technologies will be in focus at the SEMI Industry Strategy Symposium (ISS Europe), 31 March - 2 April 2019 in Milan, Italy, as top European executives, researchers and academics gather to explore solutions to the region’s most pressing strategic, economic and social challenges. Ahead of ISS Europe, SEMI spoke with Mark Purdy, managing director and chief economist at Accenture Research, about Accenture’s Business Futures – four different future worlds set in 2025 based on the collision of trends across demographics, geopolitics, technology, and economics – and what these futures will mean for markets, workforces, operating models and industry value chains. SEMI: At ISS Europe in Milan, you will kick off the symposium highlighting market opportunities of the digital economy and how companies must adapt to competitive challenges. What inspired Accenture’s Business Futures four world scenarios?Purdy: The impetus for our Business Futures really stemmed from a certain dissatisfaction with current approaches to thinking about the future. We were struck by the following puzzle. First, there is no shortage of techniques for looking at the future, from forecasting to trends analysis to conventional scenarios. Second, most decision-makers have more or less the same access to information on global trends. Yet, time and again, we hear stories of businesses going bust or facing major challenges precisely because they failed to anticipate major changes in their industry.The paradox is that we have so much information, but so little real understanding of how the future actually unfolds. So that set us thinking about how to develop a new approach, based on a combination of detailed trend analysis, expert input and creative storytelling – which is what we call “Business Futures.” SEMI: Of demographics, geopolitics, technology, and economics, which trend do you see as particularly critical?Purdy: Actually, the essence of our Business Futures thinking is that it is the collision or combination of different trends – across economics, technology, demography, etc. – that shapes future outcomes, rather than individual trends per se. To a certain extent we tend to become fixated on specific trends and this can lead us astray or cause bad decision-making. For example, in the early 2000s many people saw very favorable trends in the U.S. economy – strong capital inflows, rapidly rising consumer spending, surging stock markets, and rising home ownership rates. Each trend in isolation looked strong and sustainable. But we failed to see how the combination of these trends was fueling risky financial innovation that would eventually lead to the financial crisis and great recession.Technology of course is a key trend. We are seeing tremendous advances in next-wave technologies such as robotics, machine learning, intelligent objects, 5G and virtualization. But we can only truly understand the impact of the technologies – and the business opportunities and challenges they create – by viewing them against a wider backdrop of changes in society, demography, geopolitics and economics. That is what Business Futures strives to do.SEMI: What will these different futures mean for markets, workforce, operating models and industry value chains?Purdy: There will be profound changes in how we think about all of these areas. Markets will become much more personalized and interactive. Technology will be increasingly integrated with humans, fueling innovation in areas such as personalized healthcare and preventative medicine. Our notions of distance and capacity will be upended, as new virtualized services enable new ways of reaching underserved customers. Consumers will become increasingly involved in the creation and design of products and services. New methods of innovation, powered by AI and virtualization, will come to the fore. New entrants will come from unexpected quarters, enabled by new technology. The upshot will be massive disruption and disintermediation of value chains across many sectors.SEMI: What can Europe do to prepare?Purdy: There are no simple answers, and the correct course will vary by country, but there are some basic things to get right. First, different countries need to understand their comparative advantage – for example, whether it is in services, new technologies, advanced manufacturing or resources – and work with the grain of these different futures. Second, countries need to ensure that they have the basic conditions – regulation, organizational adaptability, workforce flexibility, skills, and innovation infrastructure – to capitalize on the productive potential of new technologies such as AI, virtual reality, and the Internet of Things (IoT). Third, we need to create educational systems and workforce learning methods that emphasize creativity, problem solving and innovation – precisely the skills that will be most needed in an age of intelligent machines. SEMI: What are your expectations for the summit in Milan and for the future?Purdy: I’m very much looking forward to the ISS Europe Summit in Milan. As an economist, I believe we are at a pivotal moment in the semi-conductor industry, driven by waves of technological change and rising geopolitical frictions and uncertainty. With so many industry leaders and experts coming together at the Summit, I’m confident that our discussions will help point a way forward!Mark Purdy is managing director of economic research at Accenture Research. His research examines issues at the intersection of economics, technology and business. He has published widely in tier-1 media and specialised publications on topics such as China’s economy, emerging-market geographic strategy, inclusive economic growth, business futures and the economic impact of new technologies such as the Internet of Things and artificial intelligence. A graduate of Trinity College Dublin, he speaks on these topics at conferences and seminars around the world.Serena Brischetto is a marketing and communications manager at SEMI Europe.

For nearly two decades, Sean Ding, CTO and chief scientist of Alibaba Cloud IoT, has worked in software and algorithm architectures, sensing, semiconductors, systems and cloud computing – all areas that have contributed to the rise of the Internet of Things (IoT). It’s no surprise, then, that Alibaba is leading next-generation innovation for the IoT. Ding will bring his expertise to his role as moderator of Brave New World - MSIG Conference on AI+IoT 2019, a half-day forum March 20, 2019, at SEMICON China in Shanghai, China. Maria Vetrano of SEMI spoke with Ding about technologies key to the IoT era including MEMS, sensors, artificial intelligence (AI), edge gateways and cloud computing. SEMI: MEMS sensors are widely used in IoT devices. What is the relationship between AI and MEMS sensors?DING: While MEMS sensors and AI will increasingly co-reside in end-user devices, I do not recommend adding AI next to the sensor (in the same package). That’s because designers continue to use the ASIC for signal conditioning, so A/D converters are still required. Rather, we should look to edge gateways to carry the majority of the workload, including deep learning, because this reduces system complexity and power consumption.SEMI: Why are smarter sensors shifting data processing and analytics to the edge of IoT devices?DING: Data processing and analytics are very important for IoT devices, but we need to focus on understanding the data, parameter calibration and more. The MEMS sensor industry should leave big data analytics to edge computing and cloud computing because AI requires deep learning, demanding a huge amount of data.The challenge is to find the sweet spot for data processing right next to the sensor element.SEMI: What is China’s evolving role in innovation in MEMS sensors for IoT devices?DING: At present, the MEMS community in China needs to figure out how to innovate instead of copying existing technologies, a low-margin business that will not help to grow the industry. One reason why I am so pleased to see the MSIG Conference on AI+IoT in China is that it will encourage greater creativity in the MEMS community in China, and this will ultimately lead to Chinese companies and R D institutions leading innovation rather than copying it.SEMI: What is the right approach to combining smart MEMS sensors with AI in IoT devices? Why is this important for both domestic Chinese and international markets?DING: Combining data from sensors with cloud-edge computing is the right approach. As sensor companies increasingly provide end-to-end solutions, such as “sensor+ firmware + SaaS + app,” we will realize easier and faster integration of sensors in IoT applications.This is incredibly important because China today is the world’s biggest market for IoT hardware. China has 2,000-plus design houses, 200-plus OEMs and thousands of distributors. That said, we still see a highly fragmented market that will benefit from a faster integration methodology.Faster integration of MEMS sensors and AI/machine learning for IoT hardware will benefit designers in international markets as well.SEMI: What do you hope MISG Conference on AI+IoT attendees will take away from the forum? DING: MEMS sensors are highly fragmented, reflecting the highly fragmented applications in which they play. The MEMS sensors industry should figure out how to provide one-stop-shopping solutions for vertical markets. This will speed the scalability of applications and expedite the growth of sensor production. Sean Ding (柯镇) will moderate Brave New World - MSIG Conference on AI+IoT 2019 at SEMICON China on Wednesday, March 20, 2019, at Kerry Hotel Pudong in Shanghai, China.This conference has been organized by the MEMS Sensors Industry Group (MSIG). Register today to connect with Sean Ding and featured speakers at the event.Speakers at the MSIG Conference on AI+IoT 2019 at SEMICON China include: Welcome and Introduction / 欢迎辞Carmelo Sansone, Director, MEMS Sensors Industry Group (MSIG), a SEMI technology community AI Needs Accurate Data – MEMS Sensors Can Provide It / MEMS传感器为人工智能提供真实数据Andrea Onetti, Group VP of Analog MEMS Group, GM of MEMS Sensor Division, STMicroelectronics Enhanced IoT Edge by Smart Sensors / 智能传感器助力IoT边缘智Bennini Fouad, Regional President Asia Pacific, Bosch Sensortec Horizon AI Processor Solution, Enable Industries in AI Time / 地平线AI芯片解决方案，赋能千万业Carl Zhang 张永谦, General Manager/VP, Smart Chip Solutions Division, Horizon Robotics Inertial Sensors in AI Applications / 运动传感器AI应用案例Ben Lee 李彬 , CEO, mCube Ultra-Low-Power Solutions: an Ecosystem Approach / 超低功耗的生态链解决方案Carlos Mazure, IEEE Fellow, Chairman Executive Director, SOI Industry Consortium High-Integrity, Fault-Tolerant Open Inertial Measurement Platform for AI-based Vehicle Automation / 适用于人工智能车辆自动控制的高集成及容错的惯性测量开放平台Dan Dempsey, Senior Director of Automotive, ACEINNA Maria Vetrano is a public relations consultant at SEMI.

Technologies promising huge growth such as Artificial intelligence (AI), 5G, machine learning, high-performance computing, and telematics are ratcheting up pressure on semiconductor manufacturers in the race among product makers to accelerate time to market and capture share. To support rapidly evolving end markets for these and other technologies that are key drivers of industry growth, chipmakers are boosting semiconductor performance, producing more wafer sizes and improving manufacturing efficiency.At the same time, chip manufacturers must enable unprecedented end-product reliability for exploding markets such as automotive and healthcare markets where, with lives at stake, products can’t afford even the slightest lapse in reliability. In response, chip suppliers are retooling their manufacturing processes to support 3D stacking, package-level integration and miniaturization. But they must do more. Bringing high efficiency to all phases of manufacturing including design and materials is the new imperative. The key to quality management is not in the traditional post-production testing and damage control but in prevention. Delivering the highest quality and reliability must start in the earliest stages of production with manufacturing and testing design – an approach that reduces not only the cost of downstream testing but minimizes product defects that can damage a supplier’s credibility and lead to lost business.To that end, SEMI has launched its Quality Assurance Special Interest Group (SIG) consisting of representatives from industry leaders such as Infineon, NXP, TSMC, UMC, ASE, Unimicron, and GCE. The group's goal is to establish quality requirements spanning the supply chain to meet new, higher reliability standards and help safeguard Taiwan’s competitive edge in the global microelectronics industry. Meeting for the first time earlier this month, the companies exchanged ideas for improving quality management in semiconductor manufacturing and ultimately deliver the reliability the market needs.The company representatives unanimously agreed that the first step is to ensure a QA-friendly environment with quality requirements for various stages of chipmaking ranging from design, manufacturing, packaging and testing to even PCB and CCL production. The SEMI Quality Assurance SIG this year plans to build on its current membership by enlisting companies from various fields to address critical areas of reliability including statistical process control, surface-mount-technology-based board level reliability control, and 0 dppm quality control for automotive chips. SEMI Quality Assurance Special Interest Group consists of leading companies in the industry, including Infineon, NXP, TSMC, UMC, ASE, Unimicron, and GCE. “SEMI’s comprehensive platform of exhibitions, programs, forums, trade meetings and matchmaking events is instrumental in bringing together key industry players to enhance quality management practices and meet the growing reliability requirements of the end markets we serve,” said Terry Tsao, chief marketing officer at SEMI and president of SEMI Taiwan. “The Quality Assurance Special Interest Group is a shining example of how SEMI continues to support the crucial role of Taiwan’s semiconductor industry in the international community.”For more information about the SEMI Quality Assurance Special Interest Group or to become a member, please contact Emmy Yi at [email protected] Yi is a marketing specialist at SEMI Taiwan.

SEMI has long promoted the industry collaboration that has contributed to the rise of the smart digital world we live in today. A world where data is being generated continuously by systems, gadgets, and sensors around us – often referred to as the Internet of Things (IoT). In our personal lives, most of us have smartphones, smart watches, smart TVs and smart cars, and we live in smart homes and smart cities generating huge amounts of data.In the work world, data and analytics are now influencing almost every industry including healthcare, government, financial services, construction and transportation. This data has the potential to transform our lives and make our world even smarter – if we can communicate and process this data, and use it to come up with actionable recommendations or actions. Artificial Intelligence (AI) and Machine Learning (ML) techniques have generated much excitement precisely because they offer us ways to realize the full value of data by harnessing it and transforming it into active intelligence.Data-intensive technologies are required to store, communicate and analyze data. And it all starts with innovation in microelectronics chips and systems spanning processors, memory, sensors, radios and other devices, presenting a huge opportunity to producers of these technologies. However, with Moore's Law beginning to slow, technology paths and innovation options are diverging. Companies must swiftly assess these options in order to develop competitive offerings. But the technological complexity and divergence makes it increasingly expensive or even unaffordable for many companies to track and pursue these options.The good news is that cost-effective early assessment is possible through pre-competitive collaboration that can produce new and often unexpected cross-disciplinary insights by overcoming traditional silos in industry and academia. Unfortunately, important collaborative industry platforms, such as the International Technology Roadmap for Semiconductors (ITRS), have folded, opening a collaboration gap in the global microelectronics ecosystem.As part of its mission to help companies connect, collaborate, and innovate, SEMI has built a collaborative, cross-supply-chain platform – the Strategic Innovation Platform (SIP). The goal is to provide early and comprehensive assessment of future technologies that are five to eight years away from commercialization. The assessment identifies not just technical barriers but also manufacturing and supply-chain constraints to implementing new technologies. SIP brings together the entire microelectronics ecosystem including strategic technology thought leaders, subject matter experts, technology and application developers, academia, researchers, start-ups and government. With more than 2,100-member companies spread across the global electronics manufacturing supply chain, SEMI is uniquely positioned to enable this critical collaboration. Award-Winning First ProjectThe inaugural SIP project assessed key drivers of future technologies. A key finding was that fast, efficient interconnects between devices and components are critical to the system performance important to customers and users, implying that system-level optimization is required. For data-intensive applications, interconnects have emerged as a key bottleneck for both performance and power in various circuits and systems in part because the slowing of Moore’s Law has decelerated advances in individual device performance, and in part because systems are becoming more complex, requiring heterogeneous integration.To address this challenge, SIP brought together industry experts from ASE Inc., Dow Chemical, Lam Research, Qualcomm and Xilinx to assess the future impact of interconnects for data-intensive applications. SEMI also involved Stanford University professors to collaborate on modeling and simulation. Through this unique cross-disciplinary collaboration, SIP developed a realistic model to evaluate the system-level performance of single-chip systems, as well as multi-chip systems – including traditional 2D packages, high-performance 2.5D systems that use interposers, and futuristic 3D systems. SIP also explored supply chain challenges in business continuity, manufacturability, Environment, Health and Safety (EHS) and the regulatory environment. SEMI worked with a broad range of industry partners to ensure that the model parameters accurately reflected realities on the design and factory floors to ensure usable results. Experimentation has become ever more expensive, with one industry player reporting that “it costs us $100 million to do a good experimental evaluation.” Accurate models can go a long way toward reducing the cost of technology assessment. The SIP collaboration produced key quantifiable insights including comparisons that highlight the benefits and limitations of various materials being explored for future interconnects, and of architectures under consideration for future data-intensive applications. For example, the current workhorse for artificial intelligence (AI) platforms – 2.5D technology – delivers a 4X improvement over 2D packaging but falls short of providing the orders-of-magnitude improvement that future AI/ML applications may require. These findings enable the industry to begin to identify ways to optimize 2.5D architectures, transition to 3D heterogeneous integration for performance-critical applications in the medium term, and to eventually evaluate new paradigms such as neuromorphic and quantum. The project findings were presented late last year in the form of two research papers at Electronics System-Integration Technology Conferences (ESTC) and International Microelectronics Assembly and Packaging Society (IMAPS) recently. One received the “Best Paper of the Session” award at IMAPS – a recognition that affirms the power of a collaborative platform such as SIP to produce valuable insights to address the growing technology complexity within the microelectronics industry. The microelectronics industry is on the cusp of a historic inflection point, where it could fuel the rise of emerging applications in AI/ML and IoT, and can grow into a trillion dollar industry over the next several years. More importantly, the industry is poised to help solve some of society’s most complex problems in areas including healthy living, climate change and transportation. No company can do this alone, and pre-competitive platforms such as SIP are key both to accelerating innovation through cross-disciplinary collaboration, and to reducing costs for individual companies. Please contact Tom Salmon at [email protected] or Pushkar Apte at [email protected] for more details and to get involved in future projects.Tom Salmon is vice president of Collaborative Technology Platforms. Pushkar Apte is a strategic technology advisor at SEMI.

The more than 53,000 people who flocked to SEMICON Korea last month were treated to a motherlode of insight into the future of the semiconductor industry as 470 companies exhibited innovative technologies in more than 2,000 booths. But the annual event’s most arresting numbers came in keynotes and other presentations pointing to the extraordinary industry growth that lies ahead.“It is no exaggeration to say that 90 percent of the world’s data has been generated in the last few years,” said Jim Feldhan, president of Semico Research. “This explosive growth of data is expected to continue. That's why server shipments will grow by 20.3 percent, or 30 million units, this year alone.”Feldhan said that the Internet of Things (IoT) will be a chief driver of semiconductor industry growth, with IoT expected to be applied in areas as varied as automotive, smart cities, edge computers, finance, architecture, agriculture and healthcare. For its part, artificial intelligence (AI) will start to exercise human-like judgment. Feldhan noted that in many instances in these fields, “it is more accurate to apply AI and vision systems than to rely on traditional decision-making.”Yoon Jong Lee, senior vice president of DB HiTek, predicted that the Internet, AI and 5G will drive market growth. “Looking back over the past 30 years, semiconductor market growth was powered by PCs, the Internet and cell phones, yet last year memory accounted for 35 percent of total semiconductor sales, more than double the figure in 2016,” he said. He predicted that, in 2019, the foundry sector will outstrip the semiconductor market in growth, noting that the average growth rate of the semiconductor industry is expected to be 4.1 percent, compared to 7.1 percent for the foundry market. Clark Tseng, director of SEMI, reported that the strong semiconductor growth in 2018 is unlikely to continue in 2019 due to the decline in memory pricing, as well as mobile and PC demand. “Demand for semiconductors is likely to decline in the first half as the industry is still digesting inventory and rebound in the second,” Tseng said. Semiconductor industry growth headwinds include decreases in high-end smartphone purchases, PC demand and demand for DRAMs for servers in data centers, Tseng said. Declines in economic growth and consumption in China and the U.S.-China trade war will also contribute to a slowdown. However, Tseng noted that, over the long term, technology innovation will continue and that the semiconductor industry’s prospects remain bright.One key innovation will be the elimination of AI’s reliance on Internet connections in the future. In his opening day keynote, Eunsoo Shim, senior vice president at Samsung Electronics, emphasized that AI technology that operates without the Internet in the future is essential. “We are developing 'on-device AI' technology that incorporates AI algorithms in products such as smartphones and autonomous vehicles,” he said. "When on-device AI technology is implemented, it reduces reliance on the Internet, battery consumption, and data latency.” Reducing latency will significantly improve device response time.Walden C. Rhines, CEO Emeritus of Mentor, a Siemens business, predicted that AI will fuel rapid memory growth. The memory semiconductor (DRAM, NAND flash) market is expected to see a temporary slowdown this year, with the market expected to rebound in 2020. Rhines said that memory could be seen as an early market with rapid future growth, citing memory market super-booms in 1995 and 2000.“Memory production has not decreased since 1995 or 2000,” he said. “Although memory prices will temporarily fall this year after significant market growth in 2017 to 2018, the market will continue to grow as memory production increases,” he said. Rhines added that “although memory prices will drop by about 10 percent this year, he believes prices will increase 6 percent next year.” He also predicted the steady growth of the non-memory semiconductor market as AI technology matures and China’s investment in fabless companies continues.Indeed, SEMICON Korea speakers made it clear that concerns about the growth of the semiconductor industry are expected to be short-lived. While overall growth is likely to slow in 2019, the industry is expected to rebound steadily – powered by the semiconductor industry paradigm shift led by AI, IOT, and autonomous driving – and reach a new high of nearly $541 billion in 2020.Jaegwan Shim is a marketing specialist at SEMI Korea.

Jason Jelinek, a software technical manager at John Deere Electronics Solutions, has parlayed his more than two decades of embedded software engineering experience into commercializing controls and sensing technologies for rugged/harsh environments, including agriculture/off-road and aerospace. During his keynote at the upcoming FLEX and MEMS Sensors Technical Congress 2019, February 18-21 in Monterey, Calif., Jelinek will address the driving need for advanced sensing technologies that will fuel the continued growth of autonomy in agriculture.SEMI’s Maria Vetrano asked Jelinek to help FLEX/MSTC attendees understand his vision of autonomy in agriculture, which heavily leverages advanced sensing technologies to help farmers master equipment logistics, handle vehicle- and fleet-level operational efficiency, and manage the entire lifecycle of crops.SEMI: Did autonomy in agriculture start with autonomous equipment, such as tractors and combines?JELINEK: Automation, the first step on the road to autonomy, has been occurring in agriculture for a long time. Over the past 100 years, automation has dramatically reduced manual effort and simplified jobs in farming, allowing operators to focus more on administrative and other aspects of their work.The evolution of the combine is a good example of automation in agriculture. Long ago farmers would use a scythe to cut down the crop before bundling or stacking it up. Later they would manually thresh and winnow the crop to get the grain. Over time, we developed windrowers to cut the grain, threshing machines to separate the grain from the chaff, and winnowing machines to get only the grain. Combines now “combine” all those steps to go from grain on the stalk in the field to grain in the hopper. One person in a combine can do the work that once required many people and animals — all in a much shorter timeframe. We are now looking at automating harvesting to maximize yield and reduce fuel consumption. The AUTOTRAC feature on John Deere machines is a recent example. AUTOTRAC divides a field into rows based upon the parameters of the machine in operation, supporting hands-free driving with very high accuracy. It allows consistent, accurate rows for tilling, planting, crop treatment and harvesting, saving considerable time, improving overall quality and freeing the operator to do other work while in the vehicle.The Exact Emerge and Section Control features (which also use AUTOTRAC) will spur greater future autonomy. Control over both the seed spacing (Exact Emerge) and when the machine drops seeds (Section Control) prevents overseeding and provides the right seed-spacing for optimal crop production.As we look to the future, sustained growth in automation of jobs will enable the development of fully autonomous equipment. Currently, however, skilled operators are still closely involved in job management and execution. To realize greater autonomy, we will need machines that make the decisions once made by people.SEMI: How will autonomy in agriculture change the ways that we grow and harvest food — and even affect when we sell it?JELINEK: Autonomy will lead to more efficient production, reducing fuel, fertilizer, herbicide and water requirements. It will also enable fewer people to do more of the work.Let’s start with conditions that are hard, even impossible, to control: weather and staffing.While farming is still tied to the weather — and will remain so for some time — more efficient operations will allow tilling, planting, spraying and harvesting of fields to occur in shorter time windows that more easily match conducive weather conditions.There is also a human-resource issue: The agricultural industry must compensate for population decline in the rural areas where farmers operate. Doing more with less is essential for agriculture to continue to meet the rising food and clothing demands of the world’s population.SEMI: To what degree will we see artificial intelligence in autonomous agricultural systems?JELINEK: While autonomous systems had their start at the vehicle level, they will one day move to the entire fleet, providing suggestions on when the owner should execute operations. Autonomous systems may also help owners to decide when to store or sell crops, based on market conditions, operating costs and desired margin levels. That’s the initial level of artificial intelligence that I foresee.SEMI: How can sensing improve autonomy in agriculture?JELINEK: The challenges we face in agriculture are many, but technology will help us meet them. We must transfer responsibility for operations and decision-making from the skilled operator to the intelligent machine. Through increased use of sensing, we can gather large amounts of data, which autonomous agricultural systems will process, communicate and interpret to streamline jobs and boost agricultural production.SEMI: What would you like FLEX/MSTC attendees to take away from your presentation?JELINEK: I would like FLEX/MSTC attendees to understand the environment in which agricultural sensors need to operate. We need sensing solutions that will survive and thrive in rugged, outdoor variable environments to support the automation that will fuel autonomy.I would also like to engage suppliers in the application of current technology to meet our sensing needs.Jason Jelinek will present Autonomy in Agriculture at FLEX/MSTC on Tuesday, February 19 at 9:00 am.Register today to connect with him at the event. To learn more, click here.MSTC Flex 2019 is organized by the MEMS Sensors Industry Group (MSIG) and FlexTech. Maria Vetrano is a public relations consultant at SEMI.

Last year the industry posted another remarkable double-digit revenue growth year. IC shipments eclipsed one trillion units for the first time and continued to enable an ever-expanding array of silicon intensive-applications.2018 was also a pivotal year of transformation at SEMI. Setting our sights firmly on building more value for SEMI members, we doubled down on priorities I established this time last year. We advocated intensively on global trade policies, industry talent needs, and critical environment, health and safety (EHS) concerns. To underpin our efforts around talent, we took the bold step to reinvigorate the industry’s identity with a dynamic image campaign. Above all, we targeted critical industry-wide issues to help us realize the ambition of becoming a trillion-dollar industry in the next decade. Workforce DevelopmentRedefining our approach to talent development in 2018 was and remains a top priority. A diverse, highly skilled workforce is crucial to the industry’s ability to innovate. Last year we ramped up a number of SEMI High Tech U (HTU) programs to inspire young people and attract them to careers in high-tech manufacturing. To date, more than 130,000 students have been touched by HTU – through student or teacher programs.Over the past year, we designed a new university outreach program and established partnerships with 100 institutions. We established Workforce Pavilions at SEMICON events in Southeast Asia, the U.S., Taiwan, Europe and Japan for students to explore career opportunities and meet with recruiters. We thrilled at seeing sponsors hire young talent at SEMI events. This year, all SEMICONs worldwide will feature Workforce Pavilions.SEMI also formalized its commitment to Diversity and Inclusion (D I) with the establishment of a D I council to shape new programs including the recently launched Spotlight on SEMI Women. To localize and fully optimize our D I programs, we established regional workforce councils in every region we serve. We unveiled the SEMI Mentoring Program to support students and young professionals on this journey by facilitating one-on-one mentoring relationships with industry professionals. Hundreds of mentees have enrolled. But we still need more mentors. I urge you to join the program. During the year, SEMI also expanded its workforce staff and developed a comprehensive workforce strategy with programs that engage students as early as elementary school and inspires them through high school and college. The program provides pathways to professional careers, building a pipeline to fill the short-term and long-term talent needs of the industry. Industry Image CampaignAs we developed the comprehensive workforce development program, we knew we had to refresh the industry’s image and appeal to the next generation through contemporary media and communications channels. So we recently launched a bold, innovative campaign to raise industry awareness and attract students and recent graduates to careers in semiconductor manufacturing.Our You’re Welcome campaign is a novel, creative approach that blends entertainment, media and storytelling to excite students about the industry. The campaign went viral immediately and within weeks had more than 5.5 million social media impressions and 2.3 million video views.Trade Policy AdvocacyRising trade tensions between the U.S. and China catapulted global trade policy to the forefront of industry concerns in 2018. Since the tariffs have taken force, semiconductor companies have faced higher costs, greater uncertainty, and difficulty selling products abroad. The tariffs have forced many SEMI member companies to pause or rethink their investment strategies.SEMI quickly engaged U.S. policymakers and provided resources for SEMI members. We formed a member trade task force, staged trade compliance seminars in China, and convened meetings with over 110 U.S. congressional, agency and administration officials, and provided testimony on the importance of the free trade to the industry.SEMI continues to educate policymakers about the critical importance of free and fair trade, open markets, and respect and enforcement of IP for all players in the global electronics manufacturing supply chain. As part of this initiative, we distributed “10 Principles for the Global Semiconductor Supply Chain in Modern Trade Agreements” and encouraged their adoption in various trade negotiations. These principles outline the primary considerations for balanced trade rules that benefit SEMI members around the world, strengthen innovation and perpetuate the societal benefits of affordable microelectronics.Environment, Health and SafetyEnvironmental regulations are proliferating globally even as advanced semiconductor manufacturing technology relies increasingly on a host of new materials. With dozens of new fabs and fab line upgrades, our industry must align on best practices, sensibly respond to materials restrictions, and renew efforts toward sustainable manufacturing.That’s why the revitalization of SEMI EHS efforts became another priority in 2018. Two months ago, we hosted the inaugural EHS Summit at SEMI Headquarters. Fully, 70 EHS professionals and company executives met to form the basis for the future SEMI EHS program.The Year AheadDespite a softening in the market, compounded by Apple’s first-ever announcement of a revenue decline in 16 years, a geopolitical whirlwind on trade and an extended shutdown of much of the U.S. government, the future is bright.At SEMI’s annual Industry Strategy Symposium (ISS 2019) in Half Moon Bay, Calif. in early January, the sense of optimism was palpable. In her keynote address, Dr. Ann Kelleher, Sr. VP and General Manager, Technology and Manufacturing Group, at Intel, observed that data is powering the fourth industry revolution and the expansion of compute. With customers expecting continual improvements in applications, Kelleher highlighted the tremendous opportunity for the chip industry to meet these expectations.At ISS 2019, we announced a Memorandum of Understand between SEMI and imec. The MOU will enable us to accelerate our members’ engagement in SEMI’s Smart vertical market platforms, in particular Smart MedTech and Smart Transportation. Our partnership with imec will also allow us to boost SEMI Standards activities in non-CMOS technologies, deepen technology roadmap efforts and augment our SEMI Think Tank initiative in thought leadership at a global level.Over the course of this coming year, will we begin our global rollout of key building blocks of our comprehensive workforce development program to engage schoolchildren as young as 10 and learners all the way to veterans who return to the workforce. We are now able, with the invaluable help of our Workforce Development Council and the passionate engagement of many SEMI member companies, to offer a solution to the talent crisis in our industry.We will continue to be the leading voice for our members and the end-to-end semiconductor supply chain across Talent, Trade, Tax and Technology as we work to ensure free, fair trade that protects IP while preserving vital access to markets to grow the supply chain. Vertical Market PlatformsOur vertical market platforms are an important part of this growth. For example, in Smart MedTech, SEMI looks forward to working with the Nano-Bio Materials Consortium to advance human monitoring technology for telemedicine and digital health after winning $7 million to fund the renewed program. In Smart Transportation, we will leverage the Global Automotive Advisory Council (GAAC) we formed last year to represent the full automotive supply chain and the Smart Transportation and Smart Automotive forums featured at all our SEMICON events to enable the industry to identify and seize opportunities in autonomous driving. At ISS 2019, Sujeet Chand of Rockwell Automation noted that “digitization will grow faster in the next 10 years than it did in the past 50,” a trend calling for semiconductor fab architectures that transform data into business value. We will continue to bring the industry together at our Smart Manufacturing venues to help uncover ways to deploy deep learning, edge computing and other Smart technologies to deliver this value and meet the challenges of automation as artificial intelligence’s (AI) sprawling influence reshapes industries including manufacturing.I am filled with optimism and thrilled about the opportunities I see on the horizon for our members as we build on our 2018 accomplishments to enable your prosperity in 2019 and beyond. My heartfelt thanks to all of you for your participation in our programs and events.I look forward to another successful year as we connect, collaborate and innovate together!Ajit Manocha is president and CEO of SEMI.

Constant coverage of an invigorating topic like machine intelligence in the media often urges us to consider its use in EDA technology. As is often the case, there are many myths and falsehoods that consume our time and effort when trying to apply machine intelligence to EDA. This article aims to uncover the myths and to provide helpful advice on applying machine intelligence to your EDA project or product.Value PropositionFirst, there needs to be a clear value proposition for adding machine intelligence to an EDA product. Using machine intelligence to create a me-too product adds no value. EDA customers are too busy to understand or care about an EDA tool’s underlying technology. They just want to use the tool and get results. If the tool delivers value, if it delivers tangible benefits, then they’ll use it. Otherwise, they won’t.Currently, EDA tool developers are already experimenting with AI and machine intelligence without considering this fundamental truth – without a higher-end objective. AI must deliver something better or new, whether a speed advantage, a performance advantage, new features, new insights, or perhaps even something pleasantly surprising. Before you write a single line of AI-enhanced code, you need to clearly understand how AI will enhance the product. What is the value proposition?Use ModelThere’s a major barrier to customer adoption of AI and machine intelligence technology for EDA tools: EDA users are averse to make decisions based on probabilistic results. Instead, half a century of EDA tool use has conditioned them to expect deterministic outcomes from their tools.Back in 2003, a prominent visionary and EDA investor was quoted in an interview, saying: “If I open my eyes five years from now, all static analysis in VLSI will be statistical.” Many EDA luminaries have been proven wrong over time for betting that EDA users will accept statistical results. As enthusiastic as I am about using machine intelligence to improve EDA tools, I must urge caution based on the history of EDA failures that employed a probabilistic use model. Decision-makers and EDA tool users want to see deterministic answers to questions about yield or slack, not probabilistic ones.Our experiences at Paripath in developing the PASER (Paripath Accelerated Simulation Environment) tool also bear this out. We discovered that delivering results 50x faster but with 92% accuracy was simply not good enough for end users. EDA users only started to use PASER when its answers became 98+% accurate. To be adopted in the production flow, the tool had to deliver 99% accuracy.Data EngineeringThere are specific ways to achieve these accuracy goals. The first is data engineering. Machine intelligence is a new approach to EDA tool development and it needs to be trained on a data set. If the data is poor or incomplete, training will create an inaccurate model. Fundamental software-development rules still apply. Garbage in, garbage out.Without good training data, there’s no way for you to build good neural-network models. If you train a model with garbage data, you’ll get a garbage model. You must cleanse the data before you use it for training. Otherwise, the model will draw inaccurate conclusions and customers will not use your tool. The model is not to blame here. The model’s not wrong. The problem lies in poor data engineering, poor data cleansing, and a lack of discipline to prepare input data.High DimensionalityNext, machine intelligence has a unique ability to quickly solve problems of high dimensionality. Pure EDA problems often have high dimensionality. Over the years, EDA developers have perfected the art of segmenting the problems into sequencing solutions with lower dimension. Machine intelligence technology can handle problems with thousands of dimensions, but you need to be careful when tackling problems that have high dimensionality. Too many dimensions can produce confused or inaccurate results with AI and deep-learning technology.It helps to visualize the problem and to analyze the data set before using the data to train an AI-enhanced EDA tool. Several visualization methods can help. For example, t-SNE (t-Distributed Stochastic Neighbor Embedding) lets you reduce a data set’s dimensionality from a very large number to a much lower number. Figure 1 shows a high-dimension dataset with a dimensionality of 2000, which has been reduced to a low dimensionality of 3. Figure 1: Visualizing the Data Set with Lower Dimensionality Reducing the dimensionality of a data set to 3 using t-SNE and visualization allows you to quickly see whether the data set defines an easy or a difficult problem. If the problem is difficult, you’ll likely need to lower the problem’s and the data set’s dimensionality before using the data to train a neural network.Technology SelectionOne factor that determines whether it will be easy or difficult to incorporate machine intelligence into your EDA tool is your choice of AI development tools. AI researchers have developed a long list of frameworks, libraries, and languages that they use to develop AI and machine-learning software. Frameworks and libraries such as TensorFlow, Caffe and MXNet are most popular for developing deep-learning models.However, these tools are not yet popular with the EDA development community. The languages of choice in the EDA community are traditionally C and C++ for development and Tcl for prototyping and creating user interfaces. The rest of the software world has moved on to newer development languages such as Python, Java, R, and such. Moreover, machine-learning development segments into two distinct processes: training (i.e. generating the model) and inference (i.e. using the model).Another question to consider is where to generate the model – at the vendor site or the customer site?Consequently, fitting AI and deep-learning development into EDA development environments can feel like fitting a square peg into a round hole. You may need to create corners in your hole.EDA is a very small player in the overall software market. Relatively few software developers are familiar with writing EDA tools. It’s best to select AI and deep-learning development tools that can provide some sort of interface that’s compatible with EDA’s development tools of choice. Some AI frameworks have lower-level C and C++ interface layers that provide a familiar entry point for experienced EDA developers.At Paripath, we chose TensorFlow for exactly this reason. TensorFlow has a lower-level C/C++ interface. Although the resulting development path becomes a longer one using this approach, it’s a more familiar path for EDA developers and therefore it’s a path that can ultimately lead your EDA development team to success. An elaborate study of comparing these frameworks has been published in the book Machine Intelligence in Design Automation.Integration into Legacy SystemsWhen you understand the value that you expect machine intelligence to add to your new EDA tool, when you’ve cleansed and then analyzed the data set, and when you have selected an appropriate set of development tools, you’re finally ready to add machine intelligence to your EDA development. There are two use models for AI-enhanced EDA tools. The first uses a trained model to guide the EDA tool’s decision-making. In this use case, the trained neural network doesn’t change. The software’s accuracy doesn’t improve with use unless the company that developed the EDA tool retrains the underlying neural network. This use case follows the familiar, existing use case associated with EDA tools developed using deterministic algorithms.For the second use case, the end user is able to retrain the underlying neural network, which allows the EDA tool to produce better, more accurate results over time. This use case produces a win/win situation because end users are able to hone their tools and improve them over time, without help from the EDA tool vendor’s application engineers. If the retrained models are also sent back to the EDA developer for incorporation into newer versions of the tool, all users benefit from other users’ training data.It’s not clear how you’d support this second use case in the current EDA business environment where most data sets are proprietary and are carefully guarded. Most large EDA tool customers want to keep their data in house under tight control. Even with this somewhat restrictive situation, however, EDA tools benefit from the incorporation of machine intelligence because each EDA tool customer can customize the tool and improve its results.Machine intelligence has much to add to EDA tools’ capabilities. Only time will tell if the customers want and will accept these new capabilities. Rohit Sharma, founder and CEO of Paripath Inc., is an engineer, author and entrepreneur. He has published many papers in international conferences and journals. He has contributed to electronic design automation domain for over 20 years learning, improvising and designing solutions. He is passionate about many technical topics including machine learning, analysis, characterization, and modeling. It led him to architect guna - an advanced characterization software for modern nodes. Sharma has written a book titled “Machine Intelligence for Design Automation.” You can download code examples and other information here.Note from SEMI-ESD Alliance: ESD Alliance’s Interoperability Committee brings together the industry to discuss interoperability. By focusing the efforts of the electronic system design community onto key compute operating systems, the Interoperability Committee seeks to define a stable, interoperable environment for tools and streamline the resources required to support these environments. The EDA Industry OS Roadmap presents guidelines to EDA vendors and customers for compute platforms to target for design starts. Learn more and view the OS Roadmap overview at our website.

This year, SEMI ISS covered it all – from a high-level semiconductor market and global geopolitical overview down to the neuro morphic and quantum level. Here are key takeaways from the Day 1 keynote and Economic Trends and Market Perspectives presentations.In the opening keynote, Anne Kelleher from Intel pointed to the huge growth of data, with fabs collecting more than 5 billion sensor data points each day. The challenge, Kelleher noted, is to turn massive amounts of data into valuable information. Moore’s law is not dead. New models of computing benefit still from Moore’s law and advances in Si/CMOS technologies for conventional, deep learning, neuro morphic and quantum computing.With customers expecting continual improvements in applications, the question is whether the chip industry is moving fast enough to meet these expectations, Kelleher said. A broad supply chain, equipment and materials innovations, and attracting the “best of the best” college graduates to fuel innovation is key, she said.In the economic trends session, Nicholas Burns (ambassador ret.) from Harvard University pointed out that we will see a major shift in power. The U.S. will remain the major world power over the next 10 years, but we will see a major shift in power in the next coming decades as the gap with countries like China, Russia and India continues to narrow.Duncan Meldrum from Hilltop Economics said that we are passing the peak growth of economic cycle. He warns that a more likely outlook is that a global growth recession is developing. Although semiconductor MSI growth will see a noticeable slowdown in 2019 and 2020, the semiconductor industry is still healthy over the longer term.Bob Johnson from Gartner sees demand shifting from consumer to commercial applications with higher ROIs and budgets. AI, IoT and 5D are the major enablers. He sees structural changes in the semiconductor industry especially for memory but also for Moore’s law with increasing costs and fewer players.The DRAM markets shows volatility and NAND market may be negative in 2019 but non-memory are expected to accelerate mainly because of increasing content and some price hikes.Overall Gartner expects good long-term growth with a CAGR (2017 to 2022) of 5.1%, outpacing 2011 to 2016 CAGR of 2.6%. After a strong 2018 with 13.4% revenue, he forecasts a slower 2019 with 2.6% growth followed by a 8% growth in 2020 and negative growth rate in 2021.Andrea Lati of VLSI went “Back to fundamentals” in his presentation about the industry. VLSI sees a downside bias due to slowing global economy, tariffs, and trade wars. Future drivers are data economy, cloud, AI and automotive.As memory leads the 2019 slowdown, analog, power, logic and other sectors remain in positive territory. VLSI lowered its semiconductor equipment forecast for 2018 from 20% (Jan. 2018) to 14% (Dec. 2018) but increased its sales outlook from 8% to 15% in 2018. VLSI expects revenue to slow into the first half of 2019 but increase to over 4% in the second half of the year, resulting in total 2019 drop of 2.7%. Semiconductor equipment sales are expected to drop from 14% in 2018 to -10% in 2019.Michael Corbett of Linz Consulting, covering wafer fab materials in the years of 3D scaling, sees these as good times for the industry. His outlook for wafer fab materials is bullish based on strong MSI and because wafer fab materials suppliers are getting bigger because of M As.In the Market Perspective session, Sujeet Chand of Rockwell Automation pointed out that as more and more data is generated, the problem is how to get value of all the data collected. There is a need to create the right architecture for machine learning and AI and big data is increasingly being replaced by contextual/structured data. He expects Industry 4.0 to drive foundries to become smaller, more flexible and more productive.In the Technology and Manufacturing session, Aki Sekiguchi of TEL addressed process challenges in the age of co-optimization. The semiconductor industry continues to expand, driven by massive growth of interconnected devices, with heavy demand for processing power and storage. He expects an exponential increase of data from about 40ZB in 2018 to 50ZB in 2020 to 163 ZB in 2026.Major technologies such as DRAM, 3D NAND and logic are dealing with scaling challenges. The density of DRAM (Mb/chip) is plateauing according to 2015 to 2020 trend data, with DRAM is in need of EUV. Memory capacity demand is leading to increasing layers and higher aspect ratios that is concern for 3D NAND and mainly for plasma etch. With Logic already implementing 3D structures, it appears to be in a solid position. Buddy Nicoson of Micron talked about his 50 years in the industry and looked ahead to the next 50. The anchors – quality, cost, scale and speed – won’t change. It has been a great journey so far with unprecedented opportunities and challenges ahead of us. We are getting into a convergence (specialization, integration) and solution-based phase. We will see some inflection points in the coming years, with the best yet to come.Christian G. Dieseldorff is senior principal analyst in the Industry Research and Analysis group at SEMI in Milpitas, California.